Excessive Agency is an OWASP LLM Top 10 risk (LLM06) covering the damage an AI agent can do when it has more functionality, permissions, or autonomy than its task requires. It is an access problem, not a model problem: the fix is scoped tools, least-privilege permissions, and a human in the loop for high-impact actions.

Runtime guardrails evaluate and enforce policy on an AI agent's actions in real time, at the moment of execution, rather than reviewing logs after the fact. They can allow low-risk actions automatically, block disallowed ones, and pause high-risk actions for human approval before anything runs.

Prompt injection is an attack that hides malicious instructions in content an AI agent processes, such as a document, email, or tool response, so the agent executes them as legitimate tasks using its own access. Indirect prompt injection needs no direct system access, only content the agent will read.

Agent identity gives an AI agent its own accountable identity, distinct from the human who launched it but tied back to that person through the identity provider. These non-human identities can outnumber human ones many times over, and without them there is no way to attribute an agent's actions or revoke its access cleanly.

The terms security, platform, and IT teams actually need to govern AI agents in production.

AI agent governance is the practice of controlling what autonomous, tool-using AI agents are allowed to do across their lifecycle: giving each agent an identity, scoping its permissions, enforcing policy at runtime, and logging every action. It is distinct from model safety, which governs what a model says rather than what an agent does in production.

Shadow AI is any AI tool, assistant, agent, or MCP connection an employee runs without security review or approval. It spreads because AI adoption outpaces policy, leaving usage that security teams cannot see, scope, or audit.

The Model Context Protocol (MCP) is an open standard that lets an AI agent discover and call external tools in a uniform way, similar to an API. It moves data between an agent and a tool but enforces no authentication or authorization by default, which is why enterprises add a governance layer on top.

AI security focuses on protecting artificial intelligence systems, models, and the data they rely on from manipulation, misuse, and evolving cyber threats. This section explores key approaches to securing AI and using AI securely within enterprise environments, from safeguarding training data to mitigating prompt injection and model exploitation risks.

An MCP gateway is a single control point that sits between every AI agent and every tool it connects to. It authenticates each connection, decides what the agent is allowed to do, and records every call, collapsing scattered agent-to-tool connections into one governed entry point.

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Network security focuses on protecting the integrity, confidentiality, and accessibility of data as it travels across networks. This section provides insights into key principles, technologies, and best practices to help build a secure and resilient network.

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Cybersecurity focuses on safeguarding digital systems, networks, and sensitive data from unauthorized access, disruption, and evolving threats. This section offers insights into key cybersecurity principles.

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Cloud security involves protecting data, applications, and services hosted in cloud environments from unauthorized access, breaches, and other cyber threats. This section explores key strategies to ensure a safe and reliable cloud infrastructure.

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